Open-Weight Models

Model

study-classification

Tony Y.

A small Mocov3 implementation for Classification, packaged with an explicit configuration and an initialization checkpoint. The small variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adam with a constant warmup schedule. These are starting values in the script…

Model

study-lightweight-multimodal

Macolombo

This is an exploratory note for Lightweight Multimodal. It records the intended comparison, likely confounders, and reproducibility requirements before any benchmark result is reported. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with notes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware…

Model

study-lightweight-multimodal

M. Garcia

A structured set of research notes on Lightweight Multimodal, with concrete evaluation references and open questions. Plans and hypotheses are kept separate from completed results. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with review.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and…

This repository contains the checkpoint for STVG-R1, the first reinforcement learning framework for spatial–temporal video grounding. We finetuned Qwen2.5-VL-7B-Instruct with constructed Visual Prompt Data. If you use STVG-R1 or this checkpoint in your research, please cite

An offline English and Hinglish command interpreter for home devices and registered industrial/robotics devices. It converts one instruction into a catalog-validated JSON intent. The package includes a trained 87,181-byte action model, a 1,002,414-device SQLite catalog, a Python API, an HTTP service, a browser playground, Docker files, and reproducible training and evaluation data. The runtime was previously packaged locally as edge-command-docker-v3-ui. The system resolves device names through explicit identifiers, aliases and SQLite FTS5 search. It prioritizes action patterns and capability checks, with a stored count-based unigram/bigram classifier for the fallback scoring path. The…

Model · Any to any

support

David Belton

https://huggingface.co/collections/DavidAU/turbo-brilliance-ai-constructs-50x-reasoning-instruct To support our team, sponsor, "Buy Me A Coffee", commission project(s), access premium item(s)/model(s)/module(s), model modifications, GET HELP, see a sorted list of models (380+) and more please see below. We build precision models at scale for the community. We are user-funded, and use local hardware to create, tune and deploy models worldwide. We also create systems for fine tuning, and specialized model systems too as well as "augment" current model/AI technology. - Model fine tuning across a diverse arch type of models and sizes, including MOEs/sparse moes. (over 1000) - Model…

Model

surabaya-opinion-tweet-clusters

Ketut Shridhara

topic-modeling - kmeans - fasttext - surabaya This repository contains a set of models for performing topic modeling on tweets from Surabaya. This repository contains two key components: 1. fasttext.model: A gensim FastText model trained on processed tweet text. It is used to generate semantic vector embeddings for documents. 2. kmeans.joblib: A scikit-learn K-Means model trained on the vectors produced by the FastText model. It contains the final topic cluster centroids. Load the models using gensim, joblib, and huggingfacehub. import joblib from gensim.models import FastText from huggingfacehub import hfhubdownload REPOID = "Kiuyha/surabaya-opinion-tweet-clusters" kmeanspath =…

A process reward model (PRM) for web agents, built by LoRA SFT on Qwen3.5-4B and merged into full weights. It evaluates candidate next actions of web agents at intermediate steps, given the task intent, the current page's accessibility tree (AXTree), and the previous action trajectory. Trained on the surfPRM-data dataset: 9,821 pairwise step-preference samples for web agents. Each sample contains the task intent, the current page's accessibility tree (AXTree), the previous action trajectory, the start/current URLs, and two candidate next actions grounded on the current page; the model outputs structured / / / reasoning and selects the better action. Requires transformers >= 5.6. Load as a…

Model

survey-neural-architecture-search

John Brown

This repository contains a working research note about Neural Architecture Search. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with papernotes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later…

A Llama-2-7b-chat checkpoint compressed with SVD-LLM to 60.0% of dense parameters, then given a 0.0% parameter budget of restored SVD components selected by the unknown rule. This is a research artifact from a study of how SVD compression damages safety behaviour and which component-selection rule best repairs it. It is one cell of a grid over selection rules and budgets; it is not a general-purpose chat model. This checkpoint exists to measure safety/utility trade-offs under compression. Several arms in the grid are deliberately safety-degraded relative to Llama-2-7b-chat: compression alone raises attack-success rate, and the point of the study is to quantify that and test recovery. Treat…

A Llama-2-7b-chat checkpoint compressed with SVD-LLM to 60.0% of dense parameters, then edited by 10 of 10 rounds of iterative parameter-neutral swap selected by the gapiter rule (up to 0.1% of dense parameters per round; the full run's budget is 1.0%). This is a research artifact from a study of how SVD compression damages safety behaviour and which component-selection rule best repairs it. It is one cell of a grid over selection rules and budgets; it is not a general-purpose chat model. This checkpoint exists to measure safety/utility trade-offs under compression. Several arms in the grid are deliberately safety-degraded relative to Llama-2-7b-chat: compression alone raises attack-success…

A Llama-3-8B-Instruct checkpoint compressed with SVD-LLM to 60.0% of dense parameters, then edited by 10 of 10 rounds of iterative parameter-neutral swap selected by the gapiter rule (up to 0.1% of dense parameters per round; the full run's budget is 1.0%). This is a research artifact from a study of how SVD compression damages safety behaviour and which component-selection rule best repairs it. It is one cell of a grid over selection rules and budgets; it is not a general-purpose chat model. This checkpoint exists to measure safety/utility trade-offs under compression. Several arms in the grid are deliberately safety-degraded relative to Llama-3-8B-Instruct: compression alone raises…